ABSTRACT This proposal aims to develop deep learning methods to automate the extraction of morphological imaging features relevant to knee osteoarthritis (OA), and total knee replacement. While, quantitative evaluation Magnetic Resonance Imaging (MRI) plays a central role in OA research in the clinical setting MR reports often tend to be subjective, qualitative, and the grading schemes utilized in epidemiological research are not used because they are extraordinarily time consuming and do not lend themselves to the demands of todays changing healthcare scenario. The ?Big Data? challenge and opportunity facing us makes it necessary to build enabling tools (i) to automate the extraction of morphological OA imaging features, with the aim of evaluating disease progression prediction capabilities on larger sample sizes that have never been explored before; (ii) to discover latent patterns by uncovering unexplored data-driven imaging features by the application of state of the art deep learning approaches (1); (iii) combine multi-modality imaging with clinical, functional, activity, and other data to define the trajectory of joint degeneration in OA. Leveraging the power of these state of the art techniques, and with the extraordinary availability of a large datasets of annotated images; in this project, we propose to develop an automatic post-processing pipeline able to segment musculoskeletal tissues and identify morphological OA features in Magnetic Resonance Images (MRI), as defined by commonly used MRI grading systems. Automation of morphological grading of the tissues in the joint would be a significant breakthrough in both OA research and clinical practice. It would enable the analysis of large sample sizes, assist the radiologist/clinician in the grading of images, take a relatively short amount of time, reduce cost, and could potentially, improve classification models. The availability of automatic pipelines for the identification of morphological abnormities in MRI would drastically change clinical practice, and include semi-quantitative grades, rather than subjective impressions in radiology clinical reports. In this study, we also aim to develop a complete supervised deep learning approach to obtain data-driven representations as non-linear and semantic aggregation among elementary features able to exploit the latent information hidden in the complexity of a 3D MR images, eliminating the need for nominal grades of selected features. This second aim, while being at high risk has also a potential exceptional high impact; as it departs from the classical hypothesis driven studies, and builds a novel translational platform to revolutionize morphological grading of MR images in research studies, but also is paradigm-shifting in that it may provide a more quantitative feature driven basis for routine radiological clinical reports. The clinical impact of this proposal lies in the third aim (R33 phase), in which we propose to translate the solutions developed in the R61 phase on images in the UCSF clinical archives (PACS), and plan to include also demographic and clinical data in the electronic health records, to build the models defining total knee replacements.